Hi Doug and the list,

I think I still have a problem understanding what cesvar.nii contains.  As a sanity check, I tried to create 95% confidence intervals around my CES. I found that in some voxels (or ROIs in my case) that had a p-value of less than 0.05, my 95% confidence interval for my CES included zero.  Here's one example:


>> clear
>> cesNii = MRIread('ces.nii');
>> cesVol = cesNii.vol;
>> cesVarNii = MRIread('cesvar.nii');
>> cesVarVol = cesVarNii.vol;
>> sigNii = MRIread('sig.nii');
>> sigVol = sigNii.vol;
>> pValue = 10^-abs(sigVol(4,1,1))

pValue =

    0.0199

>> ces = cesVol(4,1,1)

ces =

   -0.0028

>> cesVar = cesVarVol(4,1,1)

cesVar =

   6.4511e-06

>> ces95pctCI = ces + [ -1 +1 ] .* sqrt(cesVar) * 1.96

ces95pctCI =

   -0.0078    0.0022


Are the p-values calculated in a way that assumes non-Gaussian error of the ces, or am I otherwise doing something wrong?

Thanks,
Clark



1). Yes
2) Right, pull the scale outside of the sqrt()

doug
On 02/13/2013 03:36 PM, Clark Fisher wrote:
> Hi Doug,
>
> Thanks for the help, that seemed to worked great.   I have a few 
> follow up questions about how to process the resulting statistics.
>
> I'm still using v4.5 (can't switch to v5 yet), as I mentioned, and 
> would like to get the % signal change for certain contrasts with error 
> bars.  Looking through the listserv, I see that the values in 
> cespct.nii require correction by a scale factor in this version of 
> fsfast.  The most recent conversation I could find on this comes from 
> 2010, when you told Thomas Janssens:
>> Hi Thomas, you may have found a bug in my scale factor calculation. Can
>> you compute the new scale factor as described below? BTW, these issues
>> are fixed in version 5.
>>
>> cd bold/analysis
>> x = load('X');
>> tirf = x.flac0.ev(3).tirf;
>> Xirf = x.flac0.ev(3).Xirf;
>> scalefOld = sum(Xirf .* repmat(tirf,[1 size(Xirf,2)]));
>> TR = x.flac0.TR;
>> dt = tirf(2)-tirf(1);
>> scalefNew = sum(Xirf)*TR/dt;
> I can't find a follow up to this message. 2 questions.
> 1) The results with scalefNew look reasonable to me; do you still think that 
> this is the proper way to adjust cespct.nii in v4.5?
> 2) If I want to put error bars on my % signal change, how do I scale the 
> values in cesvarpct.nii?  I would guess that if I wanted to put a 95% CI on 
> my % signal change, it would be:
>       (cespct * scalefNew) +/- ( sqrt(cesvarpct) * scalefNew * 1.96)  and NOT 
> (cespct * scalefNew) +/- ( sqrt(cesvarpct * scalefNew) * 1.96)
> Does this sound right, or am I misinterpreting what cesvarpct.nii contains?
> Thanks again,
> Clark
>
>> Hi Clark,
>>
>> On 02/06/2013 05:55 PM, Clark Fisher wrote:
>> > Hi everyone,
>> >
>> > In a previous discussion on this list
>> > (http://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/msg17681.html),
>> > Pablo Polosecki was asking the best way to perform hypothesis testing
>> > using functional data within an ROI.  The final opinion was that it
>> > was best to average all of the time courses in the ROI, and re-run the
>> > analysis from scratch, so that all of the appropriate whitening, etc.
>> > occurred.
>> >
>> > I was considering doing this, creating a small volume (say, 10 voxels
>> > total) that had each voxel holding the average time course from one of
>> > 10 ROIs.  I would then set up and run a usual fsfast analysis using
>> > these tiny volumes.  I am using freesurfer 4.5.  I have a few questions.
>> >
>> > 1) Is this reasonable?
>> In general, it is reasonable. Though I'm curious what you are ultimately
>> trying to do. Do you want p-values for each subject in each ROI? Or will
>> you be combining the ROI beta values across subject in a group analysis?
>> If so, then the whitening will probably make little, if any, difference,
>> especially if this is a blocked design. In expectation, the whitening
>> does not affect the beta values.
>> >
>> > 2) One area where I expect to run into problems is with the whitening.
>> >  If I set -acfbins to the total number of my dummy voxels, and
>> > -acffwhm to 0, will this use the autocorrelation function of the
>> > average timecourse within each individual ROI?  Is that an appropriate
>> > approach? How sensitive will this be to the number of actual voxels
>> > averaged to get each ROI (for instance, would this bias me to finding
>> > more significant results in ROIs containing fewer voxels?).
>> That might work, but no promises! It is an appropriate approach. It
>> might be sensitive to the number of voxels if the number is small (say,
>> less than 100).
>> >
>> > 3) Are there other steps that depend upon the spatial arrangement of
>> > voxels that I am forgetting, and will these steps choke on these small
>> > volumes (or worse, fail in silent but pernicious ways).
>> Nothing I can think of off the top of my head.
>> doug
>> >
>> > Thanks for all of the help,
>> > Clark
>> >
>> >
>> > _______________________________________________
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>> -- 
>> Douglas N. Greve, Ph.D.
>> MGH-NMR Center
>> gr...@nmr.mgh.harvard.edu
>> Phone Number: 617-724-2358
>> Fax: 617-726-7422
>>
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-- 
Douglas N. Greve, Ph.D.
MGH-NMR Center
gr...@nmr.mgh.harvard.edu
Phone Number: 617-724-2358
Fax: 617-726-7422

Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting
FileDrop: www.nmr.mgh.harvard.edu/facility/filedrop/index.html
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